Robust multi-user detection based on hybrid grey wolf optimization

Ji, Yuanfa and Fan, Z . and Sun, X. and Wang, S. and Yan, S. and Wu, S. and Fu, Q. and Kamarul Hawari, Ghazali (2020) Robust multi-user detection based on hybrid grey wolf optimization. In: Cognitive internet of things : frameworks, tools and aplications. Studies in Computational Intelligence, 810 (810). Springer Nature Switzerland, pp. 237-249. ISBN 978-3-030-04946-1

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Abstract

The search for an effective nature-inspired optimization technique has certainly continued for decades. In this paper, a novel hybrid Grey wolf optimization and differential evolution algorithm robust multi-user detection algorithm is proposed to overcome the problem of high bit error rate (BER) in multi-user detection under impulse noise environment. The simulation results show that the iteration times of the multi-user detector based on the proposed algorithm is less than that of genetic algorithm, differential evolution algorithm and Grey wolf optimization algorithm, and has the lower BER.

Item Type:	Book Chapter
Additional Information:	Indexed by Springer
Uncontrolled Keywords:	Grey wolf optimization algorithm; Differential evolution algorithm; Hybrid optimization; Multi-user detection; Impulse noise
Subjects:	Q Science > QA Mathematics T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	Faculty of Electrical & Electronic Engineering
Depositing User:	Mrs Norsaini Abdul Samat
Date Deposited:	04 Nov 2019 07:39
Last Modified:	27 May 2020 06:54
URI:	http://umpir.ump.edu.my/id/eprint/25061
Download Statistic:	View Download Statistics

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